Core C, Quantitative Pharmacology and Biostatistics, will provide pharmacology and biostatistics support to all projects in the application. The Core is composed of three complementary components (quantitative analysis, modeling/simulation and biostatistics/data management) that serve the five Core aims. Aim 1: To develop bioanalytical methods to detect drug and biomarker exposure. Develop analytical methods to detect NKIR antagonist drug candidates in various biologic matrices in human and animal experiments. Develop biomarker assays (substance P and other neurokinins) to support both preclinical and clinical projects. Aim 2: To identify potential novel biomarkers of drug activity, subject characteristics correlated to therapeutic response and time-dependent markers of disease progression via metabolomic and metabonomic support. Determine metabolic differences in healthy volunteers, depressed and HIV-infected patients (with and without depression); assess metabolic time course in Neuro AIDS patients. Aim 3: To provide in silico ADME screens and decision criteria for drug candidates. Assess and rank druggability metrics generated from in vitro attributes and physiochemical data from HTS. Assess drug interaction potential for candidate combinations to be studied In animal and human trials. Aim 4: To provide pharmacokinetic (PK) and pharmacodynamic (PD) support for dose targeting in animal and human trials; develop PK, PD and disease progression models to define the therapeutic window for clinical candidates. PK/PD modeling to support dose selection for patient trials. Population-based PK/PD modeling to understand sources of variation in drug kinetics and viral dynamics. Disease progression modeling incorporating metabonomic indices along with clinical response and disease status. Clinical trial simulation to facilitate optimal sampling and trial design. Aim 5: To provide information systems, data management and biostatistics support to all projects and cores. Assist in the design and interpretation of preclinical/clinical experiments; support sample size requirements. Design and maintain a database to accommodate projects and cores. Manage, verify and maintain data; implement data sharing plan.